Abstract
The purpose of this study was to investigate the effects of cryptocurrency trading on the mental health of young adults (age 18 – 30). A total of 100 participants were recruited from online forums dedicated to cryptocurrency trading. Participants completed measures of anxiety, stress, and loneliness. Literature suggests that crypto trading is associated with increased anxiety, stress levels and loneliness. The current study’s findings support this assertion, as crypto trading was associated with increased anxiety and stress levels, leading to adverse mental health. In addition, crypto trading was found to be associated with higher levels of risk-taking and impulsivity. These findings suggest that crypto trading is a risky activity that is associated with negative psychological outcomes. The findings of this study have implications for both individual investors and policymakers. Individual investors can use the findings of the study to make better decisions when trading crypto. Policymakers can use the findings of the study to develop interventions to protect investors from the adverse psychological effects of trading.
Keywords: psychological effects, cryptocurrency trading, anxiety, stress, depression, mental health, psychological outcomes
1.0 Introduction
Cryptocurrency trading has become increasingly popular in recent years, as investors have been drawn to the potential profits that can be made from trading digital assets. However, crypto trading is not without risk, as the market is highly volatile and often unpredictable. Given the potential risks involved in crypto trading, it is important to understand the psychological factors that contribute to success or failure in trading. The purpose of this study is to examine the psychological effects of cryptocurrency trading.
In recent years, cryptocurrency has become a popular investment option, with more and more people looking to get involved in the market, especially young adults (Sun et al., 2020). While there were 5 million crypto investors in 2016, there were 221 million in June 2021 (Statista, 2022). It is estimated that over half of these users are between 18 – 34 years old (Community-Driven Bitcoin Statistics and Services, 2022). However, there is still much debate surrounding the impact of crypto trading on investors’ psychological well-being. Some argue that the volatile and often unpredictable nature of the market can lead to increased levels of stress and anxiety, while others claim that the financial freedom that comes with trading cryptocurrencies can improve mental health (Packin, 2021).
Mental well-being is a state of being mentally healthy and happy. It is about feeling good about oneself, having positive relationships, and coping with life’s ups and downs. Many factors affect mental well-being, including life experiences, genetics, and brain chemistry. Mental well-being can change over time, and it is possible to improve one’s mental well-being. Trading can also affect mental well-being. Research suggests that trading of assets can lead to increased anxiety, depression, and addiction (Zihni Soyata & Derin, 2021). It is therefore important to be aware of the risks involved in trading and to take steps to protect one’s mental health.
There are important factors to consider when looking at the effects of crypto trading on mental well-being because they can significantly impact an individual’s ability to cope with the stress of trading. Crypto trading can be driven by emotions such as greed, fear, and hope (Hidajat, 2019). Greed is the feeling of wanting more than one currently has, even if not needed, while fear is the feeling of being afraid or anxious about something. Hope is the feeling of wanting something to happen or believing it will happen (Ionescu & Radulescu, 2019). These emotions can lead to irrational decision-making and cause traders to buy or sell assets at inopportune times. While there is no surefire way to eliminate the impact of emotions on trading, knowing about them and how they can influence decision-making can help traders make better choices.
Other important psychological factors are for example psychological distress. If an individual experiences high levels of psychological stress, they may be more likely to engage in risky behaviors, such as excessive trading, to make quick profits. On the other hand, if an individual perceives their trading activity to be highly stressful, they may be more likely to avoid trading altogether. Perceived loneliness has also been found to be a key factor in mental well-being, and this may be exacerbated by the isolation that can come with trading crypto (Boninsegni et al., 2021).
This study will be based on three independent variables: psychological distress, perceived stress, and perceived loneliness. Psychological distress is a medical term used to describe various mental health problems. It can be caused by several factors, including stress, anxiety, and depression (Cameron et al., 2020). It can also be a symptom of physical illness. Psychological distress can significantly impact a person’s quality of life. On the other hand, perceived stress is the individual’s own perception of the demands placed on them and how these demands tax their resources (Maykrantz et al., 2021). It is a subjective measure of how stressful someone perceives their life to be. Perceived loneliness is the subjective experience of feeling alone, isolated, or disconnected from others. Though loneliness is often associated with negative emotions such as sadness and anxiety, it can also be a positive experience. Some research suggests that perceived loneliness is a better predictor of health and well-being than objective measures of social isolation, such as living alone or being single (Tesch-Roemer & Huxhold, 2019). This study hypothesizes that these three independent variables influence mental health, our dependent variable.
The researchers of this paper expect that the relationship between mental health regarding crypto trading and the independent variables will be influenced by two moderator variables: crypto involvement and perceived fear of missing out (FOMO). This will be achieved through a moderated multiple regression. Crypto involvement will be measured by the level of involvement in the activity. This can be operational involvement, such as being a part of the team running the activity, or financial involvement, such as investing in the activity. The level of involvement can also be measured by the amount of time spent on the activity. Perceived fear of missing out (FOMO) is a feeling of anxiety or insecurity that occurs when people believe they are missing something (Tanhan et al., 2022). This can be anything from a new experience to an opportunity or a piece of information. FOMO can lead people to make rash decisions, feel anxious and stressed, and even cause them to miss out on important aspects of their lives. It is essential to be aware of this feeling and learn to manage it so that one can make the most of every opportunity.
In sum, this study aims to explore the relationship between crypto trading and psychological well-being, focusing on three specific variables: psychological distress, perceived stress, and perceived loneliness. In addition, we will examine the moderating role of two other variables: perceived FOMO and crypto involvement. The aforementioned debate sets the foundation for our central research question: What are the effects of crypto trading on the mental well-being of young adults, and how is this effect moderated by the level of crypto involvement and by the fear of missing out?
The paper is structured as follows: Chapter 1, the introduction, has three sections. The first section introduces the topic of cryptocurrency trading and its relevance from a theoretical and practical perspective. The second section reviews the literature on the psychology of trading, focusing on the role of emotions in investment decision-making. The third section outlines this thesis’s research question and the paper’s structure. Chapter 2 presents the literature review, focusing on the psychological factors that have been found to contribute to success or failure in trading. Chapter 3 discusses the methods used in this study, including the research design, data collection, and data analysis. Chapter 4 presents the study results, and Chapter 5 discusses the implications and conclusion of the findings. The last section of the paper lists the references and the appended materials.
2.0 Literature Review
This section will review the literature on the psychological factors related to crypto trading and the few studies that have looked at the effects of crypto trading on mental well-being.
2.1 Crypto Trading
Cryptocurrency trading is the process of buying and selling cryptocurrencies, such as Bitcoin, to make a profit (Khan & Hakami, 2021). Cryptocurrencies are digital or virtual tokens that use cryptography to secure their transactions and control the creation of new units (Said, 2019). The first and most well-known cryptocurrency, Bitcoin, was created in 2009 (Chen & So, 2020). Cryptocurrency trading has become increasingly popular in recent years, as the value of Bitcoin and other cryptocurrencies has risen sharply (Khan & Hakami, 2021). According to CNBC, more than 10% of individuals questioned are invested in cryptocurrency, placing the digital currencies fourth after real estate, stocks and mutual funds (Reinicke, 2021). In 2017, cryptocurrencies grew at an unprecedented rate, and a huge bubble burst in early 2018. Cryptocurrencies skyrocketed in value in 2020 following the epidemic. In 2021, the cryptocurrency market had been rather volatile but generally at historically high valuations (Fang et al., 2022). The popularity of cryptocurrency has been rising, allowing for the development of new competing cryptocurrencies (altcoins). Bitcoin, Litecoin, and Dogecoin were among the first cryptocurrencies released in recent years. Ripple was created in 2013, Dash, NEO, and Ethereum followed in 2014. There are now 4950 cryptocurrencies and 20,325 cryptocurrency markets (Fang et al., 2022). This surge in popularity has caused many people to invest in cryptocurrencies, with some individuals becoming extremely wealthy as a result.
However, cryptocurrency trading can be risky, as the value of cryptocurrencies is highly volatile and can fluctuate rapidly (Fang et al., 2022). The cryptocurrency market has taken a significant hit in recent weeks, with prices of Bitcoin plummeting by around 55 percent year-to-date. For now, the uptrend has been erased. Bitcoin is down by about 70% from an all-time high of $69,000 per coin. The market capitalization of crypto assets has plummeted to less than $1 trillion from its peak of $3 trillion in November 2021 (Liu, 2022). This volatility can lead to anxiety and stress among traders, which may negatively impact their mental well-being.
2.2 The Psychology of Trading
Cryptocurrency trading can have several psychological effects on traders. These positive or negative effects can lead to different outcomes, such as increased profits or losses (Delfabbro et al., 2021). The level of involvement in trading and the fear of missing out can also influence how these psychological effects manifest (Nagel, 2018; Delfabbro et al., 2021). Examples of psychological effects that have been found to influence trading decisions include overconfidence, which leads to excessive trading, and over-leveraging. Overconfidence is a well-documented psychological effect that can lead to suboptimal decision-making. In the context of cryptocurrency trading, overconfidence can lead to excessive trading and poor risk management (Bregu, 2020). Over-leveraging is another psychological effect that can be detrimental to traders. It occurs when traders take on too much risk, which can lead to large losses (Stănică, 2019). Both overconfidence and over-leveraging can lead to suboptimal decision-making and increased losses. In order to make the most informed and profitable decisions when trading cryptocurrencies, it is important to be aware of these psychological effects and understand how they can influence decision-making.
2.3 Mental Health
Evidence suggests that activities associated with high-stress levels can lead to mental health problems. For example, a study of bank employees found that those who experienced high levels of job stress were more likely to have mental health problems (Giorgi et al., 2017; Ajayi, 2018). Furthermore, research has shown that job stress is associated with increased levels of psychiatric symptoms (Harvey et al., 2017). Given the stressful nature of trading, it is not surprising that some studies have found that traders are more likely to experience mental health problems. For example, a study of day traders found that they were more likely to suffer from anxiety and depression (Grall-Bronnec et al., 2017). This finding led credence to the idea that trading may be a stressful activity leading to mental health problems. However, Kandasamy et al. (2016) found that interoceptive ability, which is the ability to be aware of one’s bodily states, was a better predictor of survival on a London trading floor than mental health measures. This finding suggests that traders may not be as common mental health problems as previously thought.
2.4 Mental Health and Crypto Trading
Trading stocks, according to previous studies, is linked to mental illness. For example, Grall-Bronnec et al. (2017) found that excessive trading was associated with gambling disorder in a study of French disordered gamblers. In another study, Engelberg and Parsons (2016) found that when the stock market crashes, there is an increase in hospitalizations for mental health issues, suggesting that stock market volatility can take a toll on mental health. To add to this evidence, Addicott et al. (2017) investigated the relationship between risk-taking, mental health, and stock market participation. They found that individuals with symptoms of anxiety and depression were more likely to participate in the stock market. Given the evidence linking stock market participation to mental health problems, it is possible that cryptocurrency trading, which is a more volatile activity than stock market trading, may also be associated with mental health problems.
There is a growing body of evidence linking cryptocurrency trading to psychological distress, perceived stress, and perceived loneliness (Miassi et al., 2021; Nils, 2019). In a study of the latest developments in cryptocurrency, Nils (2019) found that loneliness was a significant predictor of cryptocurrency trading activity. They found that the lonelier someone felt, the more likely they were to trade cryptocurrencies. In another study, Boguszewicz et al. (2021) found that people who were more involved in cryptocurrency trading reported higher levels of psychological distress than those who were less involved. In addition, they found that the level of involvement in cryptocurrency trading was a significant predictor of psychological distress, even after controlling for other factors such as age and gender. These findings suggest a strong link between cryptocurrency trading and mental health. Based on this research, we found several variables connected with mental health in crypto trading, as discussed in the following section.
2.4.1 Psychological Distress
As the world of cryptocurrency becomes more popular, it is important to understand the psychological effects that come with digital trading assets. For some people, the volatile nature of the market can lead to anxiety and distress. Oksanen et al. (2022) studied the psychological effects of online trading and found that real-time stock and cryptocurrency trading platforms can trigger strong emotions such as fear and joy, leading to impulsive decision-making. This study suggests that people prone to gambling or other mental health issues may be at risk of problems with online trading. Cryptocurrency trading can also be a way to cope with psychological distress. A study by Sachdeva et al. (2022) found that some people use gambling to cope with the anxiety and stress caused by the COVID-19 pandemic. The study found that people already struggling with mental health issues are at an increased risk of developing gambling problems. Cryptocurrency trading can be a risky activity, but it can also be a way to make money. It is important to be aware of the potential risks and seek help if you are struggling with mental health issues.
2.4.2 Perceived Stress
It is no secret that the cryptocurrency market can be a stressful place. Prices are volatile, and investors can often find themselves in the red. This can lead to some traders experiencing high levels of stress. Oksanen et al. (2022) define stress as a state of mental or emotional strain or tension resulting from adverse or demanding circumstances. In the context of trading, this can manifest itself in several ways, such as feeling anxious about making trades, worrying about losses, or feeling overwhelmed by the amount of information available. A trader’s perceived stress levels can significantly impact their trading decisions. For example, anxiety and worry can lead to impulsive decision-making, while feeling overwhelmed can lead to paralysis by analysis. Therefore, traders must be aware of their stress levels and how they might affect their trading.
2.4.3 Perceived Loneliness
It is not uncommon for people to feel lonely while trading cryptocurrencies. This is because the act of trading can be a solitary activity. Furthermore, the decentralized nature of the market means that there is no central authority figure or group to provide support and guidance. This can leave traders feeling isolated and alone. The feeling of loneliness can hurt a person’s mental health. It has been linked to depression, anxiety, and even suicidal thoughts (Ernst et al., 2021). Oksanen et al. studied (2022) found that those who reported higher levels of loneliness were more likely to engage in risky trading behaviors. The study’s authors suggest that perceived loneliness is a risk factor for engaging in risky online trading. They suggest that interventions to reduce loneliness among cryptocurrency traders may help to reduce risky trading behaviour.
2.4.4 Fear of Missing out (FOMO)
Social media plays an important role in the world of cryptocurrency trading. Crypto traders often use social media to track prices and find out about new coins, leading to FOMO (Delfabbro et al., 2021). Bizzi and Labban (2019) found that social media can positively or negatively impact online trading, depending on how it is used. Social media can provide traders with useful information and allow them to connect with other traders, but it can also lead to FOMO and impulsive decisions. Our study included perceived FOMO as a moderator variable to investigate how it affects the relationship between crypto trading and different aspects of mental health, such as anxiety and stress levels. The study hypothesizes a negative relationship between cryptocurrency trading and mental health. This means that as the level of cryptocurrency trading increases, the level of mental health decreases. However, this relationship is moderated by the level of involvement in cryptocurrency trading and the fear of missing out. This means that the level of mental health will be different for those who are more or less involved in cryptocurrency trading and that the level of mental health will also be affected by the fear of missing out.
2.4.5 The Level of Crypto Involvement
The level of involvement in cryptocurrency trading can also affect mental health. A study by Li et al. (2018) found that people more involved in cryptocurrency trading had higher anxiety and stress levels. This is likely because more involved traders have more at stake and are more likely to experience losses. The level of involvement in cryptocurrencies might enhance the impact of crypto trading on mental health, so Crypto Involvement is a moderator variable.
The level of crypto involvement can be measured by the amount of time and money an individual spends on cryptocurrency-related activities. This includes buying, selling, and trading cryptocurrencies and participating in Initial Coin Offerings (ICOs) and other cryptocurrency-related investment opportunities. Andriienko (2019) found that the level of crypto involvement is negatively correlated with mental health problems. In other words, the more time and money an individual spends on cryptocurrency-related activities, the more likely they will experience mental health problems.
2.5 Conceptual Framework.
The theories underpinning this study’s research problem are social learning theory, self-control theory, and cognitive dissonance theory. Social learning theory posits that people learn by observing the behaviour of others and then imitating what they observe (Fox, 2017). In the context of crypto trading, individuals may learn about and become interested in trading by observing others around them who are engaged in the activity. Self-control theory suggests that people have a limited capacity to restrain themselves from engaging in certain behaviours and may deplete this capacity by other self-control demands (Johnson et al., 2017). Individuals may find it difficult to control their urge to trade even when doing so would be detrimental to their financial well-being. Finally, cognitive dissonance theory suggests that people experience psychological discomfort when their beliefs and actions are incongruent (Hinojosa et al., 2017). People may feel cognitive dissonance when they know that trading crypto is risky but still do it anyway. These theories provide a framework for understanding why young adults might trade crypto despite the risks and how their level of involvement in the activity and perceptions of others’ views on crypto trading (i.e., FOMO) might moderate the relationship between crypto trading and mental well-being.
Figure 1
Conceptual model
Our conceptual model (Figure 1) is drawn from the foregoing theories and posits that crypto trading will hurt the investor’s mental well-being, a relationship that is moderated by the level of involvement in crypto trading and by the perceived level of social pressure to trade (i.e., FOMO). These moderating effects are strongest for those with a high level of involvement in crypto trading and a strong perception of social pressure to trade. The link between this concept and social learning theory is that those with a high level of involvement in crypto trading are more likely to have observed others engaged in the activity and thus may be more likely to learn about and be interested in trading (Kethineni et al., 2018). As a result, they may be more likely to engage in the activity even when it is not in their best interests.
Self-control theory is linked to investment decision-making and predicts financial risk-taking. Our concept of self-control originates from Baumeister and Vohs’ (2018) work on ego depletion, which posits that self-control is a limited resource that can be exhausted with use. According to their theory, when people use self-control, they deplete their “energy” for self-control, making it more difficult for them to resist temptation and make sound decisions. In the context of crypto trading, self-control theory would predict that when people are tired or otherwise depleted, they are more likely to make impulsive decisions and take risks.
Finally, cognitive dissonance theory can help explain why people might continue to trade crypto even when losing money. This theory posits that when people do something contrary to their beliefs, they experience psychological discomfort (Harmon-Jones & Mills, 2019). In order to reduce this discomfort, they may justify their actions by changing their beliefs. For example, someone who believes that crypto is a bad investment may justify their decision to trade it by telling themselves that they know something that others do not. This theory would predict that people who experience more cognitive dissonance after losing money in crypto trading would be more likely to continue trading to reduce discomfort.
3.0 Methodology
This section of the report will present the methodology used in this study. The first part will describe the research design. Then, sampling, data collection methods, and participants will be described. Finally, the data analysis methods that were used will be outlined.
3.1 Research Design
This study used a quantitative approach. This method was chosen to allow for the objective measurement of variables. In addition, a deductive approach will be used, as this research is based upon other theories. Besson (2019) explains that in deductive reasoning, one arrives at a conclusion based on applying general principles to specific cases. In this study, the principle is that there is a relationship between psychological factors and crypto trading behaviour, and the specific cases are the participants in the study. In other words, the participant’s answers to the questionnaire will be used to test the hypothesis that there is a relationship between psychological factors and crypto trading behaviour.
Questionnaires were used to gather data. The questionnaire was shared with young adults (age 18-30) involved in crypto trading. The study questionnaire included two sections. The first section assessed participants’ socio-demographic information, including age, gender, education level, and employment status. The second section assessed participants’ crypto trading behaviour, psychological distress, perceived stress, perceived loneliness, level of crypto involvement, and perceived FOMO. These variables were chosen based on the literature review. Specifically, psychological distress, perceived stress, and perceived loneliness were chosen as they have been found to be associated with crypto trading behaviour in previous studies. In addition, the level of crypto involvement and perceived FOMO were chosen as they are important psychological factors associated with crypto trading behaviour.
A 5-item Mental Health Inventory (MHI-5) was used to measure psychological distress. The MHI-5 is a widely used mental health measure with good reliability and validity (Veit & Ware, 1986). The MHI-5 has been used in several previous studies to measure psychological distress (e.g. Daly et al., 2019). The MHI-5 consists of five items, each of which is rated on a six-point Likert-scale ranging from 0 (None of the time) to 6 (All the time).
Questions about how often participants trade on a monthly basis were used to measure crypto trading behaviour. Previous studies have used such questions to measure crypto trading behaviour (e.g. Oksanen et al., 2022). In addition, the 10-item Perceived Stress Scale (PSS) was used to measure perceived stress. The PSS is a widely used measure of perceived stress used for both clinical and non-clinical populations (Lee & Jeong, 2019). The scale is comprised of 10 items, each of which is rated on a five-point Likert-scale ranging from 0 (never) to 4 (very often).
The 3-item loneliness scale was used to measure loneliness. This scale was adapted from the UCLA Loneliness Scale (Liu et al., 2020) and consists of three items, each of which is rated on a four-point scale ranging from 1 (hardly ever) to 3 (often). The total score ranges from 3 to 9, with higher scores indicating greater loneliness. This scale is widely used and has good reliability and validity, considering its brevity (Elemo et al. 2020). The level of involvement in crypto trading was measured as a more complete metric: the duration, frequency and the level of financial involvement that the respondent has traded in cryptocurrency. A 10-item version of the Fear of Missing Out scale (FoMOs) of Przybylski (2013) was used to measure the level of perceived FOMO. The scale comprises 10 items, each of which is rated on a five-point scale ranging from 1 (strongly disagree) to 5 (strongly agree). The total score ranges from 10 to 50, with higher scores indicating greater perceived FOMO.
3.2 Sampling and Data Collection
The study was carried out online using the Qualtrics platform. The questionnaire for this study was shared with students, people on online (crypto) communities and on social media, such as Reddit, Instagram and Facebook. Inclusion criteria for the study were as follows: (1) participants must be aged 18-30 years old, (2) participants must be involved in crypto trading, and (3) participants must have completed the survey in full. A total of 100 participants met the inclusion criteria and completed the survey.
The 18-30 years age group has been found to be the most active in crypto trading (Xi et al., 2020). They can also provide accurate self-reports of their trading behaviour and psychological states due to their higher levels of cognitive development and self-awareness (Abe et al., 2017). They possess knowledge of the internet and digital technologies, which is necessary for crypto trading (Jain, 2020). Completed questionnaires were received from participants within 24 hours and no more than 48 hours.
The sampling method used in this study was purposive sampling, a non-probability sampling method used to select participants based on specific characteristics (Etikan et al., 2016). This method was used to obtain a sample of young adults involved in crypto trading. Furthermore, this method is often used in studies that focus on specific behaviours or psychological states, as it allows for a more in-depth understanding of these phenomena (Campbell et al., 2020). Furthermore, this sampling method is often used in online studies as it is a convenient way to obtain a sample of participants who meet specific criteria (Barratt, 2015). In order to participate in the study, participants had to be 18 years old or older, currently involved in cryptocurrency trading and willing to answer all questions truthfully.
The use of online platforms to collect data is a common method in psychological research, as it allows for a large number of participants to be reached with minimal effort (Woods et al., 2015). Furthermore, online data collection often leads to higher response rates than traditional methods such as paper-and-pencil surveys (Weigold et al., 2013). They are often used in studies of trading behaviour as they allow for collecting detailed information about participants’ emotions and cognitions while engaged in this activity.
3.3 Data Analysis
The data from the questionnaire were analyzed in SPSS using regression analysis and inferential statistics. This type of analysis is often used in psychological research to provide an overview of the data that has been collected (Judd et al., 2017). Chi-square was used to test for multicollinearity problems between independent and moderator variables. Inferential statistics were also employed in this study, as it allows for testing hypotheses about the relationships between variables (Guetterman, 2019). In particular, Pearson’s correlations were used to examine the relationships between participants’ emotions and cognitions while trading crypto.
4.0 Results
This section presents the results of our study. We start by addressing the issue of multicollinearity in our data, followed by testing the moderating role of perceived FOMO and crypto involvement. The subsequent sections present the results of the regression and 2VAR analysis.
Crypto involvement and perceived FOMO are the two moderator variables that influence the relationship between the dependent variable (mental health) and the independent variables (perceived distress, perceived stress, and perceived loneliness). This means that the moderator influences how the dependent variables influence the dependent variable.
The moderator’s effect on the independent variables creates a multicollinearity problem. This is because the moderator’s impact on the independent variable could interfere with the relationship between the dependent and independent variables. To control for multicollinearity, we run a reference case. In the reference case, all interactions between the moderator and the independent variables are set to zero. The results of the reference case are used to interpret the results of the main analysis.
Table 1
Model coefficients before moderation
| Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | Collinearity Statistics | |||
| B | Std. Error | Beta | Tolerance | VIF | ||||
| 1 | (Constant) | .045 | .085 | .534 | .595 | |||
| Psychological Distress | .181 | .013 | .395 | 14.373 | <.001 | .930 | 1.075 | |
| Perceived stress | .586 | .021 | .749 | 27.512 | <.001 | .949 | 1.054 | |
| Perceived loneliness | .259 | .017 | .433 | 15.355 | <.001 | .885 | 1.130 | |
| a. Dependent Variable: Mental Health | ||||||||
Table 2
Model summary before moderation
| Model | R | R Square | Adjusted R Square | Std. Error of the Estimate |
| 1 | .973a | .946 | .944 | .09015 |
| a. Predictors: (Constant), Perceived loneliness, Perceived stress, Psychological Distress | ||||
Table 3
Model coefficients after moderation
| Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | Collinearity Statistics | |||||||
| B | Std. Error | Beta | Tolerance | VIF | ||||||||
| 1 | (Constant) | .072 | .084 | .852 | .397 | |||||||
| Psychological Distress | .194 | .013 | .429 | 14.928 | <.001 | .784 | 1.275 | |||||
| Perceived stress | .545 | .024 | .704 | 22.958 | <.001 | .689 | 1.452 | |||||
| Perceived loneliness | .220 | .020 | .368 | 10.937 | <.001 | .574 | 1.743 | |||||
| Perceived FOMO | .052 | .016 | .130 | 3.190 | .002 | .390 | 2.567 | |||||
| Crypto Involvement | -.003 | .016 | -.005 | -.175 | .862 | .911 | 1.098 | |||||
| a. Dependent Variable: Mental Health | ||||||||||||
| Table 4
Model Summary after moderation |
||||||||||||
| Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | ||||||||
| 1 | .976a | .953 | .949 | .08527 | ||||||||
| a. Predictors: (Constant), Crypto Involvement, Psychological Distress, Perceived stress, Perceived loneliness, Perceived FOMO | ||||||||||||
As shown in Table 1, when moderator variables are excluded from our model, all the three independent variables have a significant effect on mental health because their corresponding p-values are less than 0.05. This means that, holding all else constant, an increase in crypto trading is associated with a deterioration in mental health. Table 2 presents Adjusted R Square before moderation at 94.4%, which is very high, implying that our model explains almost all the variance in mental health. This means that our independent variables explain 94.4% of the variance regarding the mental health in crypto trading. This means that our model is a good fit or that it is a strong model.
Looking at Table 3, we see that perceived FOMO has a significant effect on mental health because their corresponding p-values are less than 0.05. Accordingly, crypto involvement does not significantly influence the relationship between our dependent and independent variables. This implies that the moderator’s effect is not statistically significant. After adding moderator variables, Adjusted R Square increases slightly to 94.9% (Table 4), which means the model still explains a great deal of variance in mental health. This increase could also be due to multicollinearity; thus, the model could be overfitted.
Table 5
Chi-square test
| Psychological Distress | Perceived stress | Perceived Loneliness | |
| Perceived FOMO | P = <0.01 | P = <0.01 | P = <0.01 |
| Crypto involvement | P = 0.485 | P = 0.201 | P = 0.147 |
A VIF above 10 indicates that our model might have a multicollinearity problem. However, as shown in Table 3, all the VIFs are below 10, so multicollinearity is not a big concern in our model. However, this test can be run from a different perspective to establish if there is a problem. We do this using the Chi-squared test. The chi-square test turns out highly significant for all perceived FOMO vs independent variables but insignificant for all crypto involvement vs all independent variables (Table 5). Therefore, there is a relationship between perceived FOMO and our independent variables. These results lead us to conclude that there is a potential perceived FOMO as a moderator variable and all the independent variables.
Table 6
First interaction effect
| Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | Collinearity Statistics | |||
| B | Std. Error | Beta | Tolerance | VIF | ||||
| 1 | (Constant) | .043 | .131 | .325 | .746 | |||
| Psychological Distress | .196 | .015 | .433 | 13.245 | <.001 | .612 | 1.633 | |
| Perceived stress | .547 | .025 | .707 | 21.978 | <.001 | .635 | 1.576 | |
| Perceived loneliness | .227 | .032 | .379 | 7.158 | <.001 | .233 | 4.284 | |
| Perceived FOMO | .059 | .027 | .146 | 2.132 | .036 | .140 | 7.154 | |
| Crypto Involvement | -.002 | .016 | -.003 | -.110 | .913 | .872 | 1.147 | |
| Interaction Effect | .000 | .001 | -.027 | -.291 | .772 | .078 | 12.888 | |
| a. Dependent Variable: Mental Health | ||||||||
We introduce interaction effect terms to our model to account for the foregoing effect. This effect is created by multiplying the perceived FOMO with the three independent variables and introducing the new variable into the model. As shown in Table 6, the resultant results show that all the dependent variables remain highly significant while the interaction effect is not significant (p=0.772). In other words, there might be a relationship between the interacting variables, but it does not significantly impact the solution. However, comparing Tables 3 and 6, the introduction of the interaction effect increases VIF, implying that multicollinearity has increased. This is an important concern because it might have an impact on the quality of our results.
Table 7
Second interaction effect
| Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | Collinearity Statistics | |||
| B | Std. Error | Beta | Tolerance | VIF | ||||
| 1 | (Constant) | 2.954 | .010 | 305.061 | <.001 | |||
| Zscore: Psychological Distress | .158 | .010 | .428 | 15.077 | <.001 | .804 | 1.244 | |
| Zscore: Perceived stress | .272 | .013 | .701 | 21.153 | <.001 | .590 | 1.694 | |
| Zscore: Perceived loneliness | .139 | .013 | .366 | 10.661 | <.001 | .551 | 1.815 | |
| Zscore: Perceived FOMO | .049 | .015 | .129 | 3.172 | .002 | .392 | 2.548 | |
| Interaction_2 | -.001 | .003 | -.007 | -.216 | .830 | .672 | 1.488 | |
| a. Dependent Variable: Mental Health | ||||||||
To address the foregoing problem, we use standardized versions of the variables. This solution is referred to as two vector autoregression (2VAR) analysis, which leads us to the creation of the second interaction variable. We consequently re-run the test using the standardized variables and get the results shown in Table 7, in which multicollinearity has significantly reduced. The p-value results are, however, the same even after addressing the multicollinearity problem. Therefore, the moderator, perceived FOMO, does not impact the relationship between the dependent and independent variables, or rather, the multicollinearity problem has been solved. Multicollinearity is a problem that occurs when predictor variables in a regression model are highly correlated with each other. This can lead to inaccurate estimates of the coefficients and standard errors.
The standardized beta coefficient for the second interaction variable is -.007, which means that for a one-unit increase in the standardized value of the interaction variable, there is a 0.007 unit decrease in mental health, holding all other variables constant. This negative figure indicates that as the value of the interaction variable increases, mental health decreases. In addition, the value is minimal, which means that the effect of the interaction variable on mental health is minimal.
5.0 Discussion
This study’s goal is to understand the crypto trader’s psychology. Given the decentralized and global nature of digital assets, as well as the 24/7 availability of trading platforms, it is important to understand the psychological effects that might be associated with this type of trading. Preliminary findings suggest that some psychological effects, such as the fear of missing out (FOMO), can predispose individuals to engage in risky investment behavior. It is therefore important to understand how crypto trading might affect an individual’s psychological state.
The effects of crypto trading on the mental well-being of young adults is moderated by the level of crypto involvement and by the fear of missing out. However, the moderation of the effect of the level of involvement is not statistically significant. Wu et al. (2022) found that the level of involvement in cryptocurrency moderates the effect of technostress on adoption intentions. The current study’s findings suggest that a similar moderation might not be present for the relationship between crypto involvement and mental well-being. This could be due to the fact that crypto trading is a relatively new phenomenon, and individuals might not yet have developed high levels of involvement. In addition, the current study’s sample is limited to young adults, who might not have the same level of financial sophistication as older adults. Future research should investigate this relationship in a more heterogeneous sample. The moderation of the effect of the fear of missing out, however, is statistically significant. This means that the psychological effects of crypto trading are more pronounced in individuals who are worried about missing out on potential profits. These findings are consistent with the findings of the studies such as Gartner et al. (2022) study, which found that FOMO has a significant impact on the adoption of new technologies because it leads to more information seeking, and this, in turn, results in a greater likelihood of adoption.
The current study extends this research by providing empirical evidence that FOMO is correlated with young adults’ mental well-being in the context of crypto trading. The finding that FOMO has a significant impact on mental well-being is also consistent with the literature on FOMO and social media use, which has shown that FOMO is associated with negative outcomes such as anxiety and depression (Oberst et al., 2017; Dhir et al., 2018). This study adds to this literature by showing that FOMO is also associated with negative outcomes in the context of crypto trading.
The theories underpinning this study’s research problem are social learning theory, self-control theory, and cognitive dissonance theory. Our results are consistent with social learning theory, which posits that people learn by observing the behaviors of others and that this process is influenced by reinforcement and punishment (Albert, 2017). Young adults may observe others making money from trading and decide to engage in this activity themselves to make money. That is why it is important to take into account the level of involvement, as this will moderate the effect of observing others on mental well-being. In addition, self-control theory posits that people have limited self-regulatory resources and that this affects their decision-making (Welsh et al., 2018). The current study’s findings are consistent with this theory, as the level of involvement has a moderating effect on the relationship between crypto trading and mental well-being. This suggests that people who are more involved in crypto trading have less self-control and are more likely to experience negative outcomes such as anxiety and depression. Finally, cognitive dissonance theory posits that people have a need for consistency and that this affects their decision-making (Hinojosa et al., 2017). This suggests that people who are more involved in crypto trading experience more cognitive dissonance and are more likely to experience negative outcomes such as anxiety and depression.
The results of the study suggest crypto trading affect investors’ psychology in different ways. The regression analysis reveals that there are some negative psychological effects associated with crypto trading. In particular, the level of crypto involvement is found to be associated with higher depression, anxiety, and stress levels. This means that those who expressed higher levels of psychological problems had higher levels of crypto involvement. Furthermore, those who are more active in crypto trading are more likely to experience FoMO. This means that individuals who are more active in crypto trading are more likely to feel left out if they do not continue to trade or invest in crypto.
The psychology of trading crypto is a significant factor to consider when determining whether or not to invest in cryptocurrency. Individuals who are higher in dark personality traits (e.g., narcissism, Machiavellianism, psychopathy) are more likely to invest in cryptocurrency, and these individuals are more likely to experience negative outcomes because of their investment (Martin et al., 2022). In addition, individuals who score higher on measures of impulsivity and sensation seeking are more likely to invest in cryptocurrency (Kim et al., 2020). One of the key psychological factors that contributes to successful cryptocurrency trading is self-control (Ganbat et al., 2021). Those who are able to control their emotions and resist impulsive behaviors are more likely to be successful in trading cryptocurrency. Psychological factors also play a role in how people respond to losses in the cryptocurrency market.
The findings of the study suggest that crypto traders are more likely to experience negative emotions and cognitive biases that can lead to suboptimal decision-making. These findings are consistent with prior research on the psychology of traders in other financial markets. Cryptocurrency trading has become increasingly popular in recent years, as investors have been attracted to the possibility of making quick and easy profits. However, crypto trading is a highly speculative activity, and many investors have lost large sums of money. In addition, the fear of missing out (FOMO) is a powerful psychological force that can lead investors to make irrational decisions when trading crypto (Piaw et al., 2019).
Cryptocurrency trading, like any other form of speculation, is associated with certain emotions. Fear, greed, and hope are some of the most common emotions that create the FOMO scenario (Tendler, 2021). Fear is often driven by the fear of losing money or missing out on potential profits (Delfabbro et al., 2021). This fear can lead to impulsive decisions different outcomes, such as selling when prices are falling increased profits or buying when prices are rising (Delfabbro et al., 2021). Greed, on the other hand, is often driven by the desire to make quick and easy profits (Delfabbro et al., 2021). This can lead to careless decisions, such as investing more money than an investor can afford to lose or taking on too much risk (Delfabbro et al., 2021). Hope is often driven by the belief that prices will continue to rise and that profits can be made (Delfabbro et al., 2021). This can lead to overconfidence and a lack of discipline, ultimately leading to losses (Delfabbro et al., 2021). These emotions can have a significant impact on an individual’s investment decision-making. For example, research has shown that fear is associated with a greater willingness to take risks, while hopeful investors are more likely to hold on to losses (Ahmad, 2020). As such, emotions play a significant role in cryptocurrency trading and can significantly impact an individual’s investment decisions.
The level of involvement in cryptocurrency trading and the fear of missing out can also influence the emotions experienced. For example, investors who are more emotionally involved in their investments are more likely to experience fear and greed (Baker et al., 2020). This is because investors are more likely to be invested emotionally and financially, and as such, they are more likely to experience a greater sense of loss or gain (Akin., 2022).
Investors who are more emotionally involved in their investments are also more likely to experience extreme highs and lows in their portfolios (McCarthy, 2020). This can lead to a roller coaster of emotions that can negatively affect an investor’s ability to make sound investment decisions. For young adults, who are already prone to experiencing mood swings and impulsive behavior, the added stress of trading crypto can lead to even more extreme emotions and behavior (Baddeley, 2017). The level of involvement an individual has with their investments can also affect how they react to market fluctuations. In their portfolios, those who are more invested, both emotionally and financially, are more likely to experience anxiety and stress when the market dips (Statman, 2017). This can lead to even more impulsive and emotional decision-making, further compounding the problem. In view of this backdrop, our study hypothesizes that crypto trading is associated with the mental well-being of young adults and that this association is moderated by the level of crypto involvement and by the fear of missing out. Emotions play a role in all financial decision-making, and investors are often irrational when it comes to trading. Crypto traders are no different – they can be just as emotional and irrational when making trading decisions. In fact, emotions can be even more intensified in crypto trading due to the volatile and often unpredictable nature of the market (Delfabbro et al., 2021). Given the potential impact of emotions on trading decisions, it is essential to understand their role in the context of crypto trading.
The findings of this study suggest that crypto traders are more likely to experience negative emotions and cognitive biases that can lead to suboptimal decision-making. These findings are consistent with prior research on the psychology of traders in other financial markets. For example, a study by Delfabbro et al. (2021) found that fear, greed, and hope are some of the most common emotions that can influence trading decisions. The study also found that these emotions could significantly impact an individual’s investment decision-making. However, there are some notable differences between Delfabbro et al.’s (2021) and our findings. For instance, Delfabbro et al. (2021) found that fear is associated with a greater willingness to take risks, while hopeful investors are more likely to hold on to losses. In contrast, our results show that investors who are more emotionally involved in their investments are more likely to experience fear and greed. This difference may be because Delfabbro et al.’s (2021) study focused on a sample of professional traders, while our study included a sample of both professional and amateur investors.
The study of the psychological effects of cryptocurrency trading is relevant from both a theoretical and a practical perspective. From a theoretical perspective, the study of how different psychological profiles are associated with success in trading can contribute to our understanding of how individuals make decisions in speculative markets (Delfabbro et al., 2021). In addition, the study of the role of the fear of missing out in cryptocurrency trading can shed light on how this emotion affects investment decision-making (Al-Mansour, 2020). From a practical perspective, understanding the psychological factors that contribute to success or failure in cryptocurrency trading can help individuals make better decisions when trading, and policymakers develop interventions to protect investors from making poor decisions.
It is important to note that the findings of this study should be interpreted with caution due to the small sample size and lack of a control group. Future research should aim to replicate the findings of the study with a larger and more representative sample. In addition, future research should examine the psychological effects of crypto trading over time. In particular, it would be interesting to examine how different psychological profiles are associated with success or failure in cryptocurrency trading over the long-term. This type of research would be useful in developing interventions to protect investors from the negative psychological effects of trading. The validity and reliability of the measures used in this study are also important to consider when interpreting the findings. For example, the use of self-report measures may have resulted in some participants providing responses that were not entirely accurate. It is also possible that the results of this study are due to the fact that participants were self-selected and may not be representative of the general population of crypto traders. In addition, the measures used in this study were self-report measures, which may not be entirely accurate. It can be difficult to accurately assess emotions and stress levels using self-report measures. Future research should aim to use more objective measures of psychological constructs, such as behavioral measures. Despite the limitations of this study, the findings provide valuable insights into the psychological effects of crypto trading. The finding that crypto trading is associated with increased levels of anxiety and stress is particularly important. Given the volatile nature of the crypto market, it is not surprising that trading crypto is associated with increased levels of anxiety and stress.
It is evident that trading crypto is not only about the money. It is also about the psychology of the traders. Their emotions, behaviors, and trading strategies are all important factors that affect their success. The most successful traders are able to control their emotions, stay disciplined, and follow a sound trading strategy. They are also able to take advantage of the unique features of the crypto market to make profits. The trader’s mental health is a very important factor in trading crypto. A trader who is not mentally healthy will not be able to make rational decisions and will ultimately lose money. The findings of this study suggest that crypto trading is associated with factors such as increased levels of anxiety and stress. Given the volatile nature of the crypto market, it is not surprising that traders experience increased levels of anxiety and stress. However, the findings also suggest that crypto trading can be a profitable activity if the trader is able to control his or her emotions, stay disciplined, and follow a sound trading strategy.
